Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Journal of Information Systems and Informatics Jurnal Sains Teknologi dan Sistem Informasi
Ilsa Palingga Ninditama
Politeknik Prasetiya Mandiri
Computer Science & IT

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Analisis Sosio-Teknikal Disrupsi AI: Transformasi Arsitektur Pembelajaran dari Digital Assistance Menuju Human-Machine Co-Evolution di Pendidikan Vokasi Dita Rahmawati; Sinta Bella Agustina; Agung Indriansyah; Ilsa Palingga Ninditama; M Bambang Purwanto
SATESI: Jurnal Sains Teknologi dan Sistem Informasi Vol. 6 No. 1 (2026): April 2026
Publisher : Yayasan Pendidikan Penelitian Pengabdian ALGERO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54259/satesi.v6i1.7351

Abstract

This study aims to analyze the impact of artificial intelligence (AI) disruption on the shift in learning paradigms in Indonesian higher education, particularly at Prasetiya Mandiri Polytechnic PSDKU Palembang. The global phenomenon demonstrates that AI has become a disruptive force, serving not only as an administrative tool but also as a collaborative partner in the teaching and learning process. This study employs a descriptive, qualitative approach, drawing on phenomenological methods, to understand the experiences of lecturers and students in their interactions with AI. Data was collected through in-depth interviews, observations of digital activities, and documentation studies from UNESCO, OECD, and Ministry of Education and Culture reports. The data analysis was carried out thematically, focusing on four main themes: digital assistance, changing learning patterns, human–AI collaboration, and ethical challenges in education. The results indicate that AI integration has enhanced learning efficiency, increased student participation, and fostered independent learning. However, negative impacts were also observed, including technology dependence, a decline in critical thinking skills, and the emergence of ethical dilemmas related to plagiarism and algorithmic bias. This research emphasizes that AI should be placed not as a substitute for educators, but as a collaborative partner that enriches the humanistic learning process. It is necessary to strengthen digital literacy, AI ethics, and an adaptive curriculum grounded in human-machine synergy so that educational transformation in the era of technological disruption can occur in a sustainable and equitable manner.
Context-Aware Detection of Content Evidencing Illegal or Predatory Fintech Lending Practices on Indonesian Digital Platforms Using Transformer-Based Language Models Uci Suriani; Netti Herawati; Ilsa Palingga Ninditama
Journal of Information System and Informatics Vol 8 No 5 (2026): October
Publisher : Asosiasi Doktor Sistem Informasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63158/journalisi.v8i5.1903

Abstract

Illegal and predatory fintech lending practices generate digital content that may be difficult to identify using keyword-based screening because harmful practices can be expressed through formal, colloquial, and context-dependent language. This study benchmarks transformer-based language models for detecting content evidencing illegal or predatory fintech lending practices and examines whether Indonesian-specific pretraining provides an advantage over multilingual pretraining. A corpus of 10,000 Indonesian texts was collected from news, complaint platforms, regulatory reports, forums, consumer advocacy sources, and social media. The texts were annotated using six predefined indicators of harmful lending practices and normalised using a domain-specific slang dictionary and Sastrawi stemming. Multilingual BERT, IndoBERT, and IndoBERTweet were fine-tuned using weighted cross-entropy and compared with a TF-IDF logistic regression baseline. Multilingual BERT achieved the highest macro F1-score (0.9616), followed by IndoBERT (0.9582) and IndoBERTweet (0.9449), compared with 0.8974 for the baseline. Multilingual BERT missed only 9 of 413 harmful-content instances, reducing missed cases by 77.5% relative to the baseline. Indonesian-specific pretraining showed no clear advantage on the predominantly formal corpus, while stemming may reduce its benefit. The findings support transformer-based screening as a research benchmark for regulatory content monitoring, while human review remains necessary for enforcement decisions.
Co-Authors Agung Indriansyah Dita Rahmawati M Bambang Purwanto Netti Herawati Sinta Bella Agustina Uci Suriani